Python is a duck typing language. It means the data types of variables can change as long as the syntax is compatible. Python is also a dynamic programming language. Meaning we can change the program while it runs, including defining new functions and the scope of the name resolution. These give us not only a new paradigm in writing Python code but also a new set of tools for debugging. In the following, we will see what we can do […]

When we work on a machine learning project, we quite often need to experiment with multiple alternatives. Some features in Python allow us to try out different options without much effort. In this tutorial, we are going to see some tips to make our experiments faster. After finishing this tutorial, you will learn: How to leverage a duck-typing feature to easily swap functions and objects How making components into drop-in replacements for  each other can help experiments run faster Kick-start […]

Working on a machine learning project means we need to experiment. Having a way to configure your script easily will help you move faster. In Python, we have a way to adapt the code from a command line. In this tutorial, we are going to see how we can leverage the command line arguments to a Python script to help you work better in your machine learning project. After finishing this tutorial, you will learn Why we would like to […]

Serialization refers to the process of converting a data object (e.g., Python objects, Tensorflow models) into a format that allows us to store or transmit the data and then recreate the object when needed using the reverse process of deserialization. There are different formats for the serialization of data, such as JSON, XML, HDF5, and Python’s pickle, for different purposes. JSON, for instance, returns a human-readable string form, while Python’s pickle library can return a byte array. In this post, […]

Unit testing is a method for testing software that looks at the smallest testable pieces of code, called units, which are tested for correct operation. By doing unit testing, we can verify that each part of the code, including helper functions that may not be exposed to the user, works correctly and as intended. The idea is that we are independently checking each small piece of our program to ensure that it works. This contrasts with regression and integration testing, […]

Python is a neat programming language because its syntax is simple, clear, and concise. But Python would not be so successful without its rich third-party libraries. Python is so famous for data science and machine learning that it has become a de facto lingua franca just because we have so many libraries for those tasks. Without those libraries, Python is not too powerful. After finishing this tutorial, you will learn: Where the Python libraries are installed in your system What is […]

Data visualization is an important aspect of all AI and machine learning applications. You can gain key insights into your data through different graphical representations. In this tutorial, we’ll talk about a few options for data visualization in Python. We’ll use the MNIST dataset and the Tensorflow library for number crunching and data manipulation. To illustrate various methods for creating different types of graphs, we’ll use Python’s graphing libraries, namely matplotlib, Seaborn, and Bokeh. After completing this tutorial, you will […]

Datasets from real-world scenarios are important for building and testing machine learning models. You may just want to have some data to experiment with an algorithm. You may also want to evaluate your model by setting up a benchmark or determining its weaknesses using different sets of data. Sometimes, you may also want to create synthetic datasets, where you can test your algorithms under controlled conditions by adding noise, correlations, or redundant information to the data. In this post, we’ll […]